Posts tagged with: machine learning

What are the needs of Machine Learning?

Convexity, including convex optimization and formulation of problems as convex programs. Two important subsets of this are linear programming and proximal gradient-style optimization algorithms and formulations, which have a ridiculously vast array of applications for industrial engineering and machine learning.
Probabilistic modeling and inference: Graphical models and max-entropy models are the most important, and have a vast array of applications in machine learning and more structured statistical modeling. Markov Chain Monte Carlo is a terrific and amazing algorithm with a great special case called Gibbs sampling – they both present almost generic methods of Continue Reading

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Machine Learning Terms (#3)

 

As a headline note, I am not writing these staff to give all the details and the information about the headings, also I am not qualified as this much. I just trying to underline some basic facts for the ones who are interested in machine learning and searching some facts to investigate. Thus my headings just small introduction for your ML world search.

Linear Regression: a basic algorithm to estimate continuous output value by considering the attributes of the given instance according to the given instances in data-set with their attribut Continue Reading

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Some Basic Machine Learning Terms #1

If you are working on some project related to machine learning (ML) or you are a newbie researcher knowing these terms and definitions might be useful.

Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E — Tom M. Mitchell Continue Reading

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